AI Integration Engineer

Phase2 Technology; Corridor North (MD); On-site; USD 112,800 - 257,000; Full time

Job summary

Booz Allen Hamilton is seeking an AI Integration Engineer to design, deploy, and maintain the AI infrastructure that supports LLMs and distributed AI workloads. You will bridge advanced AI models with compute infrastructure and operational workflows to ensure stable deployments.

Ideal candidates have a strong background in high-performance computing, cloud infrastructure, MLOps or DevOps, and AI ecosystem integration.

Qualifications

• Bachelor’s degree in CS, Computer Engineering, or Systems Engineering is required.
• 5+ years in infrastructure engineering or system integration roles.
• 2+ years supporting large-scale AI/ML systems or GPU-centric environments.

Responsibilities

• Serve as the technical point of contact for integrating LLMs and other AI workloads across infrastructure systems and pipelines.
• Architect, deploy, and maintain scalable GPU computing environments for autonomous workflows.
• Develop, manage, and optimize CI/CD pipelines for AI deployments.
• Oversee network and infrastructure connectivity, ensuring seamless communication between distributed systems and GPUs.
• Design and secure tool-calling environments with governance and sandboxing for autonomous actions.
• Provide diagnostic and troubleshooting expertise for AI systems and monitor infrastructure.

Skills

Infrastructure engineering

System integration

Cloud platforms

MLOps / DevOps

Networking concepts

Education

Bachelor’s degree in CS/CE/SE

Tools

Kubeflow

TensorFlow Serving

LangGraph

Kubernetes

Docker

Terraform

Pulumi

Redis

Postgres

Job description

AI Integration Engineer

The Opportunity

We are seeking a highly motivated AI Integration Engineer to join our team and help design, deploy, and maintain the infrastructure that supports artificial intelligence (AI) systems, including Large Language Models (LLMs) and distributed AI workloads. This role is critical to bridging the gap between advanced AI models, compute infrastructure, and operational workflows. You will be responsible for managing AI readiness by architecting scalable infrastructure solutions, integrating complex systems, and maintaining operational excellence to ensure stable deployments of AI and machine learning applications. The ideal candidate has a strong background in high-performance computing, cloud infrastructure, MLOps or DevOps, and AI ecosystem integration.

This is an exciting opportunity to be at the forefront of AI operational infrastructure and contribute to cutting‑edge projects.

What You’ll Work On

• Serve as the technical point of contact for integrating LLMs and other AI workloads across infrastructure systems, operational tools, and application pipelines.
• Architect, deploy, and maintain scalable GPU computing environments and infrastructure required for autonomous agentic workflows, including persistent state management, long‑term memory systems such as Vector DBs, and multi‑step reasoning traces.
• Develop, manage, and optimize CI/CD pipelines for AI deployments, ensuring smooth transitions from model development to production environments.
• Oversee network and infrastructure connectivity, ensuring seamless communication between distributed systems, GPUs, virtual machines (VMs), APIs, and Command and Control (C2) tools.
• Design and secure tool‑calling environments where agents interact with external APIs, ensuring strict governance and sandboxing for autonomous actions.
• Provide diagnostic and troubleshooting expertise for AI systems, monitoring infrastructure to maintain availability, security, and performance benchmarks.
• Collaborate across engineering, data, and AI teams to align infrastructure solutions with business and operational goals.

You Have

• 5+ years of experience in infrastructure engineering or system integration roles
• 2+ years of experience supporting large‑scale AI/ML systems or GPU‑centric environments
• Experience with cloud platforms such as AWS, Azure, or Google Cloud, and their AI‑focused services, including SageMaker, GCP AI Platform, and Azure Machine Learning
• Experience with networking concepts, including TCP/IP, DNS, NGINX, load balancing, and firewalls, applied to AI model and infrastructure deployment
• Experience integrating MLOps pipelines using tools such as MLflow, Kubeflow, TensorFlow Serving, or Vertex AI, including integration of AgentOps frameworks such as LangSmith and Arize Phoenix, to monitor autonomous decision‑making paths and agent reasoning traces
• Experience with orchestration frameworks for multi‑agent systems such as LangGraph, CrewAI, or AutoGen, and managing the stateful databases required to support them, including Redis and Postgres
• Experience working with NVIDIA GPU technologies, including CUDA, NCCL, TensorRT, and DGX systems, and container or orchestration tools such as Kubernetes, Docker, Terraform, or Pulumi
• Ability to manage and optimize distributed, high‑performance computing environments, including clusters of GPUs and cloud‑based GPU instances
• TS/SCI clearance with a polygraph
• Bachelor’s degree in CS, Computer Engineering, or Systems Engineering

Nice If You Have

• Experience with AI/ML frameworks for model training and deployment such as PyTorch, TensorFlow, or Hugging Face Transformers
• Experience implementing observability and monitoring systems such as Grafana, Prometheus, and ELK, for AI infrastructure to track performance and operational health
• Experience with security practices for AI systems, including encryption, role‑based access controls, secure APIs, and compliance frameworks such as SOC 2 and GDPR
• Experience with Agentic Safety, including the implementation of Human‑in‑the‑Loop (HITL) approval gateways and automated kill switches for autonomous processes
• Experience with Vector Database infrastructure such as Pinecone, Weaviate, or Milvus, and Retrieval‑Augmented Generation (RAG) pipelines used to provide agents with contextual memory
• Knowledge of distributed computing frameworks such as Ray, Horovod, or Dask, for AI training jobs
• Knowledge of AI ethics and operational risk assessments, ensuring deployed systems align with organizational policies and standards
• Certified Kubernetes Administrator (CKA) or Kubernetes Application Developer (CKAD) Certification
• AWS Certified Solutions Architect similar Cloud Certifications
• NVIDIA Certifications such as the NVIDIA Certified Advanced GPU Infrastructure Specialist Certification

Clearance

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance with polygraph is required.

Compensation

The projected compensation range for this position is $112,800.00 to $257,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the posting date.

Identity Statement

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.

Candidate AI Usage Policy

AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in‑person or virtual) is prohibited unless permission is explicitly provided.

Work Model

Our people‑first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.

• Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
• Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
• Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.

Commitment to Non-Discrimination

All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.